Awesome Pruning

A curated list of neural network pruning and related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers and Awesome-NAS.

Please feel free to pull requests or open an issue to add papers.

Table of Contents

• Type of Pruning

• 2022 Venues

• 2021 Venues

• 2020 Venues

• 2019 Venues

• 2018 Venues

• 2017 Venues

• 2016 Venues

• 2015 Venues

Type of Pruning

Type	F	W	Other
Explanation	Filter pruning	Weight pruning	other types

2022

Title	Venue	Type	Code
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server	IJCAI	F	-
On the Channel Pruning using Graph Convolution Network for Convolutional Neural Network Acceleration	IJCAI	F	-
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization	IJCAI	F	-
Neural Network Pruning by Cooperative Coevolution	IJCAI	F	-
Recent Advances on Neural Network Pruning at Initialization	IJCAI	W	PyTorch(Author)
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness	ICML	F	PyTorch(Author)
SPDY: Accurate Pruning with Speedup Guarantees	ICML	W	PyTorch(Author)
Sparse Double Descent: Where Network Pruning Aggravates Overfitting	ICML	W	PyTorch(Author)
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning	ICML	Other	-
Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks	ICML	Other	PyTorch(Author)
Winning the Lottery Ahead of Time: Efficient Early Network Pruning	ICML	F	-
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning	ICML	F	PyTorch(Author)
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks	ICML	W	PyTorch(Author)
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks	ICML	Other	PyTorch(Author)
Fast Lossless Neural Compression with Integer-Only Discrete Flows	ICML	F	PyTorch(Author)
Fire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask PredictionFire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask Prediction	CVPR	F	-
Revisiting Random Channel Pruning for Neural Network Compression	CVPR	F	PyTorch(Author)(Releasing)
Interspace Pruning: Using Adaptive Filter Representations To Improve Training of Sparse CNNs	CVPR	W	-
When To Prune? A Policy Towards Early Structural Pruning	CVPR	F	-
DiSparse: Disentangled Sparsification for Multitask Model Compression	CVPR	Other	PyTorch(Author)(Releasing)
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning	CVPR	WF	-
Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning	CVPR	F	-
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network	CVPR	W	-
DECORE: Deep Compression With Reinforcement Learning	CVPR	F	-
CHEX: CHannel EXploration for CNN Model Compression	CVPR	F	-
Compressing Models With Few Samples: Mimicking Then Replacing	CVPR	F	PyTorch(Author)(Releasing)
Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining	ICLR (Spotlight)	W	PyTorch(Author)
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning	ICLR (Spotlight)	W	-
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning	ICLR (Spotlight)	F	PyTorch(Author)(Releasing)
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models	ICLR (Spotlight)	F	PyTorch(Author)
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients	ICLR	WF	PyTorch(Author)
An Operator Theoretic View On Pruning Deep Neural Networks	ICLR	W	-
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions	ICLR	F	PyTorch(Author)
Effective Model Sparsification by Scheduled Grow-and-Prune Methods	ICLR	W	PyTorch(Author)
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training	ICLR	Other	PyTorch(Author)
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning	ICLR	F	PyTorch(Author)
Signing the Supermask: Keep, Hide, Invert	ICLR	W	-
Plant 'n' Seek: Can You Find the Winning Ticket?	ICLR	F	PyTorch(Author)
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks	ICLR	F	PyTorch(Author)
On the Existence of Universal Lottery Tickets	ICLR	F	PyTorch(Author)
Training Structured Neural Networks Through Manifold Identification and Variance Reduction	ICLR	F	PyTorch(Author)
How many degrees of freedom do we need to train deep networks: a loss landscape perspective	ICLR	W	PyTorch(Author)
Dual Lottery Ticket Hypothesis	ICLR	W	PyTorch(Author)
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently	ICLR	W	PyTorch(Author)
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training	ICLR	W	PyTorch(Author)
Prior Gradient Mask Guided Pruning-Aware Fine-Tuning	AAAI	F	-
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition	AAAI	Other	-
Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks	AAAI	W	-

2021

Title	Venue	Type	Code
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory	NeurIPS	W	-
The Elastic Lottery Ticket Hypothesis	NeurIPS	W	PyTorch(Author)
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?	NeurIPS	W	PyTorch(Author)
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks	NeurIPS	W	-
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership	NeurIPS	W	PyTorch(Author)
Pruning Randomly Initialized Neural Networks with Iterative Randomization	NeurIPS	W	PyTorch(Author)
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration	NeurIPS	W	PyTorch(Author)
Rethinking the Pruning Criteria for Convolutional Neural Network	NeurIPS	F	-
Only Train Once: A One-Shot Neural Network Training And Pruning Framework	NeurIPS	F	PyTorch(Author)
CHIP: CHannel Independence-based Pruning for Compact Neural Networks	NeurIPS	F	PyTorch(Author)
Sparse Flows: Pruning Continuous-depth Models	NeurIPS	WF	PyTorch(Author)
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness	NeurIPS	W	PyTorch(Author)
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition	NeurIPS	F	PyTorch(Author)
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks	NeurIPS	W	PyTorch(Author)
RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks	NeurIPS	F	-
Scaling Up Exact Neural Network Compression by ReLU Stability	NeurIPS	F	PyTorch(Author)
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme	NeurIPS	F	PyTorch(Author)
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks	NeurIPS	Other	PyTorch(Author)
ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting	ICCV	F	PyTorch(Author)
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search	ICCV	F	-
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization	ICCV	F	-
Auto Graph Encoder-Decoder for Neural Network Pruning	ICCV	F	-
Sub-Bit Neural Networks: Learning To Compress and Accelerate Binary Neural Networks	ICCV	Other	PyTorch(Author)
Exploration and Estimation for Model Compression	ICCV	F	-
A Probabilistic Approach to Neural Network Pruning	ICML	F	-
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework	ICML	F	-
Group Fisher Pruning for Practical Network Compression	ICML	F	PyTorch(Author)
On the Predictability of Pruning Across Scales	ICML	W	-
Towards Compact CNNs via Collaborative Compression	CVPR	F	PyTorch(Author)
Content-Aware GAN Compression	CVPR	F	PyTorch(Author)
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks	CVPR	F	PyTorch(Author)
NPAS: A Compiler-aware Framework of Unified Network Pruning andArchitecture Search for Beyond Real-Time Mobile Acceleration	CVPR	F	-
Network Pruning via Performance Maximization	CVPR	F	-
Convolutional Neural Network Pruning with Structural Redundancy Reduction	CVPR	F	-
Manifold Regularized Dynamic Network Pruning	CVPR	F	-
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation	CVPR	FO	-
A Gradient Flow Framework For Analyzing Network Pruning	ICLR	F	PyTorch(Author)
Neural Pruning via Growing Regularization	ICLR	F	PyTorch(Author)
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations	ICLR	F	PyTorch(Author)
Network Pruning That Matters: A Case Study on Retraining Variants	ICLR	F	PyTorch(Author)
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network	ICLR	W	PyTorch(Author)
Layer-adaptive Sparsity for the Magnitude-based Pruning	ICLR	W	PyTorch(Author)
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?	ICLR	W	-
Robust Pruning at Initialization	ICLR	W	-

2020

Title	Venue	Type	Code
HYDRA: Pruning Adversarially Robust Neural Networks	NeurIPS	W	PyTorch(Author)
Logarithmic Pruning is All You Need	NeurIPS	W	-
Directional Pruning of Deep Neural Networks	NeurIPS	W	-
Movement Pruning: Adaptive Sparsity by Fine-Tuning	NeurIPS	W	PyTorch(Author)
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot	NeurIPS	W	PyTorch(Author)
Neuron Merging: Compensating for Pruned Neurons	NeurIPS	F	PyTorch(Author)
Neuron-level Structured Pruning using Polarization Regularizer	NeurIPS	F	PyTorch(Author)
SCOP: Scientific Control for Reliable Neural Network Pruning	NeurIPS	F	PyTorch(Author)
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning	NeurIPS	F	-
The Generalization-Stability Tradeoff In Neural Network Pruning	NeurIPS	F	PyTorch(Author)
Pruning Filter in Filter	NeurIPS	Other	PyTorch(Author)
Position-based Scaled Gradient for Model Quantization and Pruning	NeurIPS	Other	PyTorch(Author)
Bayesian Bits: Unifying Quantization and Pruning	NeurIPS	Other	-
Pruning neural networks without any data by iteratively conserving synaptic flow	NeurIPS	Other	PyTorch(Author)
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	ECCV (Oral)	F	PyTorch(Author)
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation	ECCV	F	-
DHP: Differentiable Meta Pruning via HyperNetworks	ECCV	F	PyTorch(Author)
Meta-Learning with Network Pruning	ECCV	W	-
Accelerating CNN Training by Pruning Activation Gradients	ECCV	W	-
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search	ECCV	Other	-
Differentiable Joint Pruning and Quantization for Hardware Efficiency	ECCV	Other	-
Channel Pruning via Automatic Structure Search	IJCAI	F	PyTorch(Author)
Adversarial Neural Pruning with Latent Vulnerability Suppression	ICML	W	-
Proving the Lottery Ticket Hypothesis: Pruning is All You Need	ICML	W	-
Soft Threshold Weight Reparameterization for Learnable Sparsity	ICML	WF	Pytorch(Author)
Network Pruning by Greedy Subnetwork Selection	ICML	F	-
Operation-Aware Soft Channel Pruning using Differentiable Masks	ICML	F	-
DropNet: Reducing Neural Network Complexity via Iterative Pruning	ICML	F	-
Towards Efficient Model Compression via Learned Global Ranking	CVPR (Oral)	F	Pytorch(Author)
HRank: Filter Pruning using High-Rank Feature Map	CVPR (Oral)	F	Pytorch(Author)
Neural Network Pruning with Residual-Connections and Limited-Data	CVPR (Oral)	F	-
Multi-Dimensional Pruning: A Unified Framework for Model Compression	CVPR (Oral)	WF	-
DMCP: Differentiable Markov Channel Pruning for Neural Networks	CVPR (Oral)	F	TensorFlow(Author)
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression	CVPR	F	PyTorch(Author)
Few Sample Knowledge Distillation for Efficient Network Compression	CVPR	F	-
Discrete Model Compression With Resource Constraint for Deep Neural Networks	CVPR	F	-
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization	CVPR	W	-
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration	CVPR	F	-
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy	CVPR	F	-
Comparing Rewinding and Fine-tuning in Neural Network Pruning	ICLR (Oral)	WF	TensorFlow(Author)
A Signal Propagation Perspective for Pruning Neural Networks at Initialization	ICLR (Spotlight)	W	-
ProxSGD: Training Structured Neural Networks under Regularization and Constraints	ICLR	W	TF+PT(Author)
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation	ICLR	W	-
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning	ICLR	W	PyTorch(Author)
Dynamic Model Pruning with Feedback	ICLR	WF	-
Provable Filter Pruning for Efficient Neural Networks	ICLR	F	-
Data-Independent Neural Pruning via Coresets	ICLR	W	-
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates	AAAI	F	-
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks	AAAI	Other	-
Pruning from Scratch	AAAI	Other	-
Reborn filters: Pruning convolutional neural networks with limited data	AAAI	F	-

2019

Title	Venue	Type	Code
Network Pruning via Transformable Architecture Search	NeurIPS	F	PyTorch(Author)
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks	NeurIPS	F	PyTorch(Author)
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask	NeurIPS	W	TensorFlow(Author)
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers	NeurIPS	W	-
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks	NeurIPS	W	PyTorch(Author)
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters	NeurIPS	W	-
Model Compression with Adversarial Robustness: A Unified Optimization Framework	NeurIPS	Other	PyTorch(Author)
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning	ICCV	F	PyTorch(Author)
Accelerate CNN via Recursive Bayesian Pruning	ICCV	F	-
Adversarial Robustness vs Model Compression, or Both?	ICCV	W	PyTorch(Author)
Learning Filter Basis for Convolutional Neural Network Compression	ICCV	Other	-
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration	CVPR (Oral)	F	PyTorch(Author)
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning	CVPR	F	PyTorch(Author)
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure	CVPR	F	PyTorch(Author)
On Implicit Filter Level Sparsity in Convolutional Neural Networks, Extension1, Extension2	CVPR	F	PyTorch(Author)
Structured Pruning of Neural Networks with Budget-Aware Regularization	CVPR	F	-
Importance Estimation for Neural Network Pruning	CVPR	F	PyTorch(Author)
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks	CVPR	F	-
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search	CVPR	Other	TensorFlow(Author)
Variational Convolutional Neural Network Pruning	CVPR	-	-
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks	ICLR (Best)	W	TensorFlow(Author)
Rethinking the Value of Network Pruning	ICLR	F	PyTorch(Author)
Dynamic Channel Pruning: Feature Boosting and Suppression	ICLR	F	TensorFlow(Author)
SNIP: Single-shot Network Pruning based on Connection Sensitivity	ICLR	W	TensorFLow(Author)
Dynamic Sparse Graph for Efficient Deep Learning	ICLR	F	CUDA(3rd)
Collaborative Channel Pruning for Deep Networks	ICML	F	-
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github	ICML	F	-
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis	ICML	F	PyTorch(Author)
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning	IJCAI	F	Tensorflow(Author)

2018

Title	Venue	Type	Code
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers	ICLR	F	TensorFlow(Author), PyTorch(3rd)
To prune, or not to prune: exploring the efficacy of pruning for model compression	ICLR	W	-
Discrimination-aware Channel Pruning for Deep Neural Networks	NeurIPS	F	TensorFlow(Author)
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks	NeurIPS	W	-
Learning Sparse Neural Networks via Sensitivity-Driven Regularization	NeurIPS	WF	-
Amc: Automl for model compression and acceleration on mobile devices	ECCV	F	TensorFlow(3rd)
Data-Driven Sparse Structure Selection for Deep Neural Networks	ECCV	F	MXNet(Author)
Coreset-Based Neural Network Compression	ECCV	F	PyTorch(Author)
Constraint-Aware Deep Neural Network Compression	ECCV	W	SkimCaffe(Author)
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers	ECCV	W	Caffe(Author)
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning	CVPR	F	PyTorch(Author)
NISP: Pruning Networks using Neuron Importance Score Propagation	CVPR	F	-
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization	CVPR	W	-
“Learning-Compression” Algorithms for Neural Net Pruning	CVPR	W	-
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks	IJCAI	F	PyTorch(Author)
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning	IJCAI	F	-

2017

Title	Venue	Type	Code
Pruning Filters for Efficient ConvNets	ICLR	F	PyTorch(3rd)
Pruning Convolutional Neural Networks for Resource Efficient Inference	ICLR	F	TensorFlow(3rd)
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee	NeurIPS	W	TensorFlow(Author)
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon	NeurIPS	W	PyTorch(Author)
Runtime Neural Pruning	NeurIPS	F	-
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning	CVPR	W	-
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression	ICCV	F	Caffe(Author), PyTorch(3rd)
Channel pruning for accelerating very deep neural networks	ICCV	F	Caffe(Author)
Learning Efficient Convolutional Networks Through Network Slimming	ICCV	F	PyTorch(Author)

2016

Title	Venue	Type	Code
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding	ICLR (Best)	W	Caffe(Author)
Dynamic Network Surgery for Efficient DNNs	NeurIPS	W	Caffe(Author)

2015

Title	Venue	Type	Code
Learning both Weights and Connections for Efficient Neural Networks	NeurIPS	W	PyTorch(3rd)

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